Shashi Kumar is working as Scientist in Photogrammetry & Remote Sensing Department of Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun. His research work is focused on SAR Remote Sensing with special emphasis on Polarimetric SAR (PolSAR), Polarimetric SAR Interferometry (PolInSAR) and SAR Tomography for structural and biophysical characterization of forest area. Shashi Kumar has supervised several research works on SAR Remote Sensing and its utilization for forest parameter retrieval. He is also actively involved in NISAR related activities to process the PolSAR & PolInSAR data and its utilization for various applications. He has carried out several research projects on SAR data and some of the major highlights are enumerated below.• PolSAR and PolInSAR based model development for stem volume and aboveground biomass (AGB) estimation of forest.• PolSAR Tomography for vertical tree structure of tropical forest.• Processing of Hybrid-Pol (RISAT-1, FRS-1) and Quad-Pol (RISAT-1, FRS-2) SAR data for semi-empirical modeling for forest AGB estimation.• Integration of PolSAR, PolInSAR and Terrestrial Laser Scanner (TLS) data for tree height estimation and modelling for forest AGB estimation.• Polarimetric modeling of Lunar Surface for scattering information retrieval using Chandrayaan-1, Mini-SAR• Hyperspectral Remote Sensing for Extraction of Aqueous Minerals on Mars and Optical Maturity Estimation of Titanium Dioxide & Ferrous Oxide on Lunar Surface.

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Urban areas due to their dynamic nature often pose serious threats to environment causing overutilization of resources like ground water. The depleting ground water table often causes land subsidence leading to cracks in buildings. This subsidence can be easily mapped using PSInSAR (Persistent Interferometric Synthetic Aperture RADAR). In urban areas, there are many buildings per square kilometer which give permanent scatter and act as good corner reflectors at boundaries right angle to ground and walls. Rudrapur city, which is the headquarters, of Udham Singh Nagar district of Uttarakhand state in India, is also a major industrial hub and attracts skilled and unskilled labour force from the adjoining areas and this is leading to an unprecedented growth of urban sprawl. The city shows sprawling dense urban settlements and huge industrial setups on the outskirts, surrounded by agricultural fields and orchards. Main source of water supply is through bore wells and tube wells. Here, it was found that over the years, ground water has been harnessed for not only household supplies but also for agriculture and industrial purposes which has led to lowering of water table down from around 33.5 m to 45.7 m. This is leading to cracks developing in buildings particularly around the industrial area. The changes over a period of a year from 4th December 2014 till 2nd December 2015, were mapped using PSInSAR technique, with X-band TerraSAR-X datasets.

Airborne synthetic aperture radar (SAR) data have been successfully used for forest height inversion; however, there is limited applicability in spaceborne scenarios due to high temporal decorrelation. This study investigates the potential of a high-resolution fully polarimetric interferometric pair of TerraSAR-X/TanDEM-X SAR data with no temporal decorrelation to analyze the backscatter and coherence response and to implement polarimetric SAR interferometry-based height inversion algorithms. The data were acquired over Barkot forest region of Uttarakhand state in India. Yamaguchi decomposition was implemented onto the dataset to express total backscatter as a sum of different scattering components from a single SAR resolution cell. Coherency matrix was used to compute complex coherence for different polarization channels. Forest areas suffered from low coherence due to volume decorrelation, whereas a dry river bed had shown high coherence. The coherence amplitude inversion approach overestimated the forest height and also resulted in false heights for this dry river bed. These limitations were overcome by implementing three-stage inversion modeling, which assumes polarization-independent volume coherence. The results were validated using ground truth data available for 49 plots, and the latter was found to be more accurate with an overall accuracy of 90.15% and root-mean-square error of 2.42 m.

Forest height plays a crucial role in investigating the biophysical parameters of forests and the terrestrial carbon. PolInSAR-based inversion modeling has been successfully implemented on airborne and spaceborne synthetic aperture radar (SAR) data. SAR tomography is a recent approach to separate scatterers in the cross-range direction and generate its vertical profile. This study highlights the potential of tomographic processing of multibaseline fully polarimetric Radarsat-2 C-band SAR data to estimate radar reflectivity at different forest height levels. A teak patch of Haldwani forest in Uttarakhand state of India was chosen as the test site to perform tomography. Since SAR tomography is a spectral estimation problem, Fourier transform (FT), beamforming (BF), and Capon-based spectral estimators were applied on the dataset to obtain the backscattering power contributions at different forest height levels. Fourier showed high backscatter power retrieval at different forest heights. The radar reflectivities at different heights were significantly reduced by BF followed by Capon. Tomographic profile of FT severely suffered from high sidelobes, which was drastically reduced by implementing BF. Capon further reduced the sidelobes and achieved a substantially improved tomographic profile. The height maps were generated for these algorithms and validated with ground truth data.

Forest height plays a crucial role to investigate the bio-physical parameters of forest and the terrestrial carbon. PolInSAR based inversion modeling has been successfully implemented on airborne and space-borne SAR data. SAR tomography, which is an extension of cross-track interferometric processing is a recent approach to separate scatterers in cross range direction, thus generates its vertical profile. This study highlighted the potential of tomographic processing of fully polarimetric Radarsat-2 SAR system to retrieve backscatter power at different height levels. Teak forest in Haldwani forest division of Uttarakhand state of India was chosen as the test site. Since SAR tomography is a spectral estimation problem, Fourier transform and beamforming based spectral estimations were applied on the dataset to obtain their vertical profiles. Fourier severely suffered from high side lobes which was drastically reduced by implementing beam-forming by taking into account the multi-looking effect at the expense of radiometric accuracy. Backscattered power values were found to be different at different height levels of the forest vegetation. Vertical profile for Fourier as well as beam-forming were also retrieved.

Oceans are considered as the important source for oil reserves and continuous activities like oil extraction and transportation may sometimes cause the accidental release of oil into the sea surface which causes a major threat to the marine ecosystems, economy and human life. The prime focus of this study is to explore the potential of the fully polarimetric SAR data and analyze the different scattering mechanisms for the oil spilled regions. In this study the fully polarized and orthorectified, L band data of UAVSAR airborne sensor is used which is captured on June 22nd 2010, during which the Deepwater Horizon oil spill occurred in the Gulf of Mexico. For the detection of oil spill different decomposition techniques such as Freeman, Yamaguchi and H/A/α are studied and classified using Wishart classification. Freeman and Yamaguchi decomposition helped in understanding the type of scattering mechanism taking place in slick covered regions, sea surface and in the presence of ships/rig. A set of polarimetric parameters such as magnitude of correlation coefficient, cross product of co polarized channels, anisotropy, alpha ,entropy and the intensity of the coherency matrix are studied which helped in distinguishing the oil spills, sea surface and the look-alikes. The Wishart classification result of Freeman and Yamaguchi decompositions showed more reliable results in comparison to the K-means classification results obtained through segmentation of combined H/A/α decomposition. The entropy, anisotropy and magnitude of correlation coefficient are dependent on the angle of incidence. At low incidence angle the entropy value of oil spills are similar to that of the sea surface whereas the magnitude of correlation coefficient which is a function of dielectric constant, increases for oil spills at low incidence angle. The polarimetric parameter, intensity of the coherency matrix utilizes the whole coherency matrix by calculating its determinant and proven to provide good discrimination between the oil spills and the sea surface.

The main objective of this study was to explore the potential of the multi-temporal PolSAR data in LULC mapping and to evaluate the accuracy of classification using single date and multi-temporal data. Multi-temporal data acquired on three different dates were used. Advanced classification techniques Support Vector Machine and Rule Based Hierarchical approaches were performed on multitemporal ALOS PALSAR data to classify features at different temporal combinations. In this study, SVM classification was applied on the derived output of Yamaguchi decomposition model, for which kernel approach of second order polynomial was used. In Rule Based Hierarchical approach, Backscattering coefficients, Yamaguchi and H/A/Alpha decomposition statistics are computed and analyzed to estimate the decision boundaries of the features to separate feature at different hierarchical levels. SVM classified the PolSAR data efficiently of single data, highest overall accuracy and kappa statistics achieved was 67.65% and 0.61 from the individual image. Rule based classified map of single date, highest overall accuracy and kappa statistics achieved was 68% and 0.67. Based on the accuracy assessment, SVM and Rule Based classification both are approximately of same accuracy but comparatively Rule Based classification was accurate temporally. Rule Based classification was further considered for multi-temporal classification and achieved high overall accuracy and kappa statistics of 80% and 0.76. This proves that multi-temporal PolSAR data helps to increase the accuracy of classification in LULC mapping.

Polarimetric SAR data has proven its potential to extract scattering information for different features appearing in single resolution cell. Several decomposition modelling approaches have been developed to retrieve scattering information from PolSAR data. During scattering power decomposition based on physical scattering models it becomes very difficult to distinguish volume scattering as a result from randomly oriented vegetation from scattering nature of oblique structures which are responsible for double-bounce and volume scattering , because both are decomposed in same scattering mechanism. The polarization orientation angle (POA) of an electromagnetic wave is one of the most important character which gets changed due to scattering from geometrical structure of topographic slopes, oriented urban area and randomly oriented features like vegetation cover. The shift in POA affects the polarimetric radar signatures. So, for accurate estimation of scattering nature of feature compensation in polarization orientation shift becomes an essential procedure. The prime objective of this work was to investigate the effect of shift in POA in scattering information retrieval and to explore the effect of deorientation on regression between field-estimated aboveground biomass (AGB) and volume scattering. For this study Dudhwa National Park, U.P., India was selected as study area and fully polarimetric ALOS PALSAR data was used to retrieve scattering information from the forest area of Dudhwa National Park. Field data for DBH and tree height was collect for AGB estimation using stratified random sampling. AGB was estimated for 170 plots for different locations of the forest area. Yamaguchi four component decomposition modelling approach was utilized to retrieve surface, double-bounce, helix and volume scattering information. Shift in polarization orientation angle was estimated and deorientation of coherency matrix for compensation of POA shift was performed. Effect of deorientation on RGB color composite for the forest area can be easily seen. Overestimation of volume scattering and under estimation of double bounce scattering was recorded for PolSAR decomposition without deorientation and increase in double bounce scattering and decrease in volume scattering was noticed after deorientation. This study was mainly focused on volume scattering retrieval and its relation with field estimated AGB. Change in volume scattering after POA compensation of PolSAR data was recorded and a comparison was performed on volume scattering values for all the 170 forest plots for which field data were collected. Decrease in volume scattering after deorientation was noted for all the plots. Regression between PolSAR decomposition based volume scattering and AGB was performed. Before deorientation, coefficient determination (R<sup>2</sup>) between volume scattering and AGB was 0.225. After deorientation an improvement in coefficient of determination was found and the obtained value was 0.613. This study recommends deorientation of PolSAR data for decomposition modelling to retrieve reliable volume scattering information from forest area.

Forests act as sink of carbon and as a result maintains carbon cycle in atmosphere. Deforestation leads to imbalance in global carbon cycle and changes in climate. Hence estimation of forest biophysical parameter like biomass becomes a necessity. PolSAR has the ability to discriminate the share of scattering element like surface, double bounce and volume scattering in a single SAR resolution cell. Studies have shown that volume scattering is a significant parameter for forest biophysical characterization which mainly occurred from vegetation due to randomly oriented structures. This random orientation of forest structure causes shift in orientation angle of polarization ellipse which ultimately disturbs the radar signature and shows overestimation of volume scattering and underestimation of double bounce scattering after decomposition of fully PolSAR data. Hybrid polarimetry has the advantage of zero POA shift due to rotational symmetry followed by the circular transmission of electromagnetic waves. The prime objective of this study was to extract the potential of Hybrid PolSAR and fully PolSAR data for AGB estimation using Extended Water Cloud model. Validation was performed using field biomass. The study site chosen was Barkot Forest, Uttarakhand, India. To obtain the decomposition components, m-alpha and Yamaguchi decomposition modelling for Hybrid and fully PolSAR data were implied respectively. The RGB composite image for both the decomposition techniques has generated. The contribution of all scattering from each plot for m-alpha and Yamaguchi decomposition modelling were extracted. The R2 value for modelled AGB and field biomass from Hybrid PolSAR and fully PolSAR data were found 0.5127 and 0.4625 respectively. The RMSE for Hybrid and fully PolSAR between modelled AGB and field biomass were 63.156 (t ha-1) and 73.424 (t ha-1) respectively. On the basis of RMSE and R2 value, this study suggests Hybrid PolSAR decomposition modelling to retrieve scattering element for AGB estimation from forest.

SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.

Polarization orientation angle (POA) shift in the backscattered SAR wave induced, due to irregularity of the target surface. Polarimetric signatures of the backscatter SAR wave gets affected by the POA shift, causes error in the decomposition modelling as shift in POA makes coherency matrix asymmetric. POA shift compensation is very necessary to avoid misinterpretation of decomposition modelling results. POA shift effect has been observed using coherency matrix and decomposition model results. This study is conducted over Dudhwa National Park in the state of Uttar Pradesh, using high resolution, TDM SAR COSSC Product of TerraSAR-X and TanDEM-X in Bistatic mode. Present study mainly focused on the comparative analysis of resultant scattering component of decomposition model before and after POA shift compensation. Shift in POA is investigated using circular polarization technique. Yamaguchi four component decomposition model is used to express total backscatter information in terms of volume, double bounce, surface and helix scattering. Volume scattering is overestimated however double bounce and surface scattering is under estimated in decomposition model due to POA shift present in the backscatter SAR wave. Different scattering mechanisms resulted after POA compensation were analyzed using 100 random points taken from forest structure. The results obtained by TerraSAR-X and TanDEM-X shows an overall increase in double bounce scattering and decrease in volume scattering component after POA shift compensation. It is observed that there is negligible effect of POA shift on surface scattering. POA shift compensation necessarily required to improve the accuracy of decomposition models used in the forest parameter retrieval applications.

Airborne SAR data has been successfully used for forest height inversion, however there is limited applicability in space borne scenario due to high temporal decorrelation. This study investigates the potential of high resolution fully polarimetric pair of TerraSAR-X/TanDEM-X SAR data acquired over Barkot forest region of Uttarakhand state in India to analyze the backscatter and coherence and to test the height inversion algorithms. Yamaguchi decomposition was implemented onto the dataset to express total backscatter as a sum of different scattering components from a single SAR resolution cell. Coherency matrix was used to compute complex coherence for different polarization channels. Forest areas suffered from low coherence due to volume decorrelation whereas dry river bed had shown high coherence. Appropriate perpendicular baseline and hence the interferometric vertical wavenumber was selected in forest height estimation. Coherence amplitude inversion (CAI) approach overestimated the forest height and also resulted in false heights for dry river bed. This limitation was overcome by implementing three stage inversion modeling (TSI) which assumes polarization independent volume coherence and the heights in dry river bed were completely eliminated. The results were validated using ground truth data available for 49 plots, and TSI was found to be more accurate with an average accuracy of 90.15% and RMSE of 2.42 m.

Fully Polarimetric SAR (PolSAR) data is used for scattering information retrieval from single SAR resolution cell. Single SAR resolution cell may contain contribution from more than one scattering objects. Hence, single or dual polarized data does not provide all the possible scattering information. So, to overcome this problem fully Polarimetric data is used. It was observed in previous study that fully Polarimetric data of different dates provide different scattering values for same object and coefficient of determination obtained from linear regression between volume scattering and aboveground biomass (AGB) shows different values for the SAR dataset of different dates. Scattering values are important input elements for modelling of forest aboveground biomass. In this research work an approach is proposed to get reliable scattering from interferometric pair of fully Polarimetric RADARSAT-2 data. The field survey for data collection was carried out for Barkot forest during November 10th to December 5th, 2014. Stratified random sampling was used to collect field data for circumference at breast height (CBH) and tree height measurement. Field-measured AGB was compared with the volume scattering elements obtained from decomposition modelling of individual PolSAR images and PolInSAR coherency matrix. Yamaguchi 4-component decomposition was implemented to retrieve scattering elements from SAR data. PolInSAR based decomposition was the great challenge in this work and it was implemented with certain assumptions to create Hermitian coherency matrix with co-registered polarimetric interferometric pair of SAR data. Regression analysis between field-measured AGB and volume scattering element obtained from PolInSAR data showed highest (0.589) coefficient of determination. The same regression with volume scattering elements of individual SAR images showed 0.49 and 0.50 coefficients of determination for master and slave images respectively. This study recommends use of interferometric PolSAR data for reliable scattering retrieval.

Synthetic Aperture Radar (SAR) is one of the most recent imaging technology to study the forest parameters. The invincible characteristics of microwave acquisition in cloudy regions and night imaging makes it a powerful tool to study dense forest regions. A coherent combination of radar polarimetry and interferometry (PolInSAR) enhances the accuracy of retrieved biophysical parameters. This paper attempts to address the issue of estimation of forest structural information caused due to instability of radar platforms through simulation of SAR image. The Terai Central Forest region situated at Haldwani area in Uttarakhand state of India was chosen as the study area. The system characteristics of PolInSAR dataset of Radarsat-2 SAR sensor was used for simulation process. Geometric and system specifications like platform altitude, center frequency, mean incidence angle, azimuth and range resolution were taken from metadata. From the field data it was observed that average tree height and forest stand density were 25 m and 300 stems/ha respectively. The obtained simulated results were compared with the sensor acquired master and slave intensity images. It was analyzed that for co-polarized horizontal component (HH), the mean values of simulated and real master image had a difference of 0.3645 with standard deviation of 0.63. Cross-polarized (HV) channel showed better results with mean difference of 0.06 and standard deviation of 0.1 while co-polarized vertical component (VV) did not show similar values. In case of HV polarization, mean variation between simulated and real slave images was found to be the least. Since cross-polarized channel is more sensitive to vegetation feature therefore better simulated results were obtained for this channel. Further the simulated images were processed using PolInSAR inversion modelling approach using three different techniques DEM differencing, Coherence Amplitude Inversion and Random Volume over Ground Inversion. DEM differencing technique calculates tree height by generating Digital Elevation Models (DEM) from interferograms in different polarizations and differences in DEM estimates the vegetation height. In CAI technique the phase of coherence is ignored and volume scattering is mainly considered for estimating height. The RVoG model considers both vegetation layer and ground interactions. In this model, the vertical distribution of scatterers do not change with the change in polarization. It was found that with vertical wavenumber values between 0.2113 to .2249 rad/m for mean incidence angle 34.226 degrees the range of tree height achieved by Coherence Amplitude Inversion and RVoG was better among the three inversion techniques.

A new approach to reconstruction of pseudo quad-polarized data from hybrid polarimetric data has been presented in this research. The algorithm is based on certain assumptions which were validated upon testing the aptness of the results and their comparison with true optical images of the region under study. This involved direct construction of the 3X3 coherency matrix from the 2X1 scattering matrices obtained from the hybrid polarimetric data. The reasonableness of the assumptions were tested by decomposing the reconstructed pseudo quad-pol data using a coherent decomposition mechanism. The data set used in this project was Level-1 FRS-1 Hybrid Polarimetric data and FRS-2 Quad-pol data of RISAT-1. Reliable scattering retrieval from SAR data involves the calibration of the data. Polarimetric calibration was performed on real and imaginary channels of the single look complex SAR data. The newly developed algorithm was implemented on calibrated data. To extract complete information of different scattering elements of any location, second order derivative of scattering matrix is the most suitable and widely used matrix. Coherency matrix of pseudo quad-pol obtained from hybrid polarimetric data using reconstruction algorithm was decomposed using Yamaguchi four component decomposition for scattering information extraction. The obtained surface, double-bounce and volume scattering were compared with the scattering elements of hybrid-polarimetric decomposition, m-alpha and decomposition of quad-pol data of RISAT-1. The comparison revealed that the results obtained were satisfactory and thus the assumptions made during the reconstruction of pseudo quad-pol data were reasonable for specific purposes. Further comparisons of results using different decompositions technique at pixel level comparison can help better understand the aptness of the algorithm.

Surface maturity estimation of the lunar regolith revealed selenological process behind the formation of lunar surface, which might be provided vital information regarding the geological evolution of earth, because lunar surface is being considered as 8-9 times older than as that of the earth. Spectral reflectances data from Moon mineralogy mapper (M3), the hyperspectral sensor of chandrayan-1 coupled with the standard weight percentages of FeO from lunar returned samples of Apollo and Luna landing sites, through data interpolation techniques to generate the weight percentage FeO map of the target lunar locations. With the interpolated data mineral maps were prepared and the results are analyzed.

100 t/ha), remains a challenging task for the researchers worldwide. The retrieval of AGB over a tropical forest area in India using Envisat advanced synthetic aperture radar C-band backscatter, interferometric synthetic aperture radar (InSAR) coherence and semi-empirical models viz., water cloud model (WCM) and interferometric water cloud model (IWCM), is studied. In process, the model parameters, i.e., backscatter from vegetation and ground, two-way tree transmissivity, and coherence from vegetation and ground were retrieved. The model training procedure to retrieve the model parameters consisted of an iterative regression of WCM and IWCM. High AGB accuracy (R2=0.73) with low root mean square error (RMSE=53.76 t/ha) was achieved through multidate weighted averaging using RMSE-based weighting coefficients and WCM. Multidate data and InSAR coherence images showed better results (R2=0.90, RMSE=35.92 t/ha) compared to individual coherence images. The InSAR coherence was found to be better for AGB retrieval than SAR backscatter as the former did not saturate for high AGB values.

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